{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5278b560",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import pickle\n",
    "import time\n",
    "import sys\n",
    "import itertools\n",
    "from tqdm.notebook import tqdm\n",
    "import open3d as o3d\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d2c75b72",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from torchvision import transforms\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "\n",
    "from scipy.spatial.transform import Rotation as R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9530e38d",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.set_printoptions(precision=6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cacfdab0",
   "metadata": {},
   "source": [
    "### Dataset utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bffa2d5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Kitti(Dataset):\n",
    "    def __init__(self, scan_dir, pose_dir, transform, test_sequence=None, split='train'):\n",
    "        self.scan_dir = scan_dir # lidar scans\n",
    "        self.pose_dir = pose_dir # ground-truth poses\n",
    "        self.split = split\n",
    "        self.transform = transform\n",
    "        self.projected = {} # dict to store transformed scans\n",
    "        \n",
    "        if self.split == 'train':\n",
    "            self.sequence_idx = '07' # TODO: make train/val data include multiple sequences instead??\n",
    "        elif self.split == 'validate':\n",
    "            self.sequence_idx = '04'\n",
    "        elif self.split == 'test':\n",
    "            self.sequence_idx = test_sequence\n",
    "            \n",
    "        self.velo_files = self.load_velo_files(self.sequence_idx)\n",
    "        self.poses = self.load_poses(self.sequence_idx)\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.velo_files)\n",
    "    \n",
    "    def __repr__(self):\n",
    "        return \"Total frames: {}, total poses: {} in {} sequence {}\".format(len(self.velo_files),\n",
    "                                                                    len(self.poses), self.split, self.sequence_idx)\n",
    "    \n",
    "    def __getitem__(self, index: int):\n",
    "        if index == 0:\n",
    "            prev_index = 0\n",
    "        else:\n",
    "            prev_index = index - 1\n",
    "\n",
    "        curr_scan, prev_scan = self.load_velo(index), self.load_velo(prev_index) # velo scans\n",
    "        \n",
    "        # if scans already projected, grab from memory; else transform scans and add to memory\n",
    "        if index in self.projected.keys():\n",
    "            curr_img = self.projected[index]\n",
    "        else:\n",
    "            curr_img = self.transform(curr_scan)\n",
    "            self.projected[index] = curr_img\n",
    "            \n",
    "        if prev_index in self.projected.keys():\n",
    "            prev_img = self.projected[prev_index]\n",
    "        else:\n",
    "            prev_img = self.transform(prev_scan)\n",
    "            self.projected[prev_index] = prev_img\n",
    "        \n",
    "        # grab poses and compute relative pose\n",
    "        curr_pose, prev_pose = self.poses[index], self.poses[prev_index]\n",
    "        rel_pose = np.linalg.inv(prev_pose) @ curr_pose \n",
    "        return curr_img, prev_img, rel_pose\n",
    "    \n",
    "    def load_velo_files(self, seq_idx):\n",
    "        sequence_dir = os.path.join(self.scan_dir, seq_idx, 'velodyne')\n",
    "        sequence_files = sorted(os.listdir(sequence_dir))\n",
    "        velo_files = [os.path.join(sequence_dir, frame) \n",
    "                              for frame in sequence_files]\n",
    "        return velo_files\n",
    "    \n",
    "    def load_velo(self, item: int): \n",
    "        \"\"\"Load velodyne [x,y,z,i] scan data from binary files.\"\"\"\n",
    "        filename = self.velo_files[item]\n",
    "        scan = np.fromfile(filename, dtype=np.float32)\n",
    "        scan = scan.reshape((-1,4)) \n",
    "        return scan\n",
    "    \n",
    "    def load_poses(self, sequence):\n",
    "        pose_file = os.path.join(self.pose_dir, sequence + '.txt')\n",
    "        poses = [] # store 4x4 pose matrices\n",
    "        try:\n",
    "            with open(pose_file, 'r') as f:\n",
    "                lines = f.readlines()\n",
    "                for line in lines:\n",
    "                    pose_vector = np.fromstring(line, dtype=float, sep=' ')\n",
    "                    pose_matrix = pose_vector.reshape(3, 4)\n",
    "                    pose_matrix = np.vstack((pose_matrix, [0, 0, 0, 1]))\n",
    "                    poses.append(pose_matrix)      \n",
    "        except FileNotFoundError:\n",
    "            print('Ground truth poses are not available for sequence ' +\n",
    "                  sequence + '.')\n",
    "        return poses  \n",
    "    \n",
    "    def visualise(self, index: int):\n",
    "        img, _, _ = self.__getitem__(index)\n",
    "        img = img.permute(1,2,0).numpy()\n",
    "        img_intensity = img[:,:,4] * (255.0 / img[:,:,4].max()) # [xyz range intensity normals]\n",
    "        img_range = img[:,:,3] * (255.0 / img[:,:,3].max())\n",
    "        \n",
    "        fig, axs = plt.subplots(2, figsize=(12,6), dpi=100)\n",
    "        axs[0].imshow(img_intensity)\n",
    "        axs[0].set_title(\"Intensity map\")\n",
    "        axs[1].imshow(img_range) # invert normalize TODO: invert or not??\n",
    "        axs[1].set_title(\"Depth map\")\n",
    "        plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "005329b5",
   "metadata": {},
   "source": [
    "### Point cloud downsampler (experiment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "37345b8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# sampling config\n",
    "n_points = 4096 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "133152d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "class PointCloudSampler():\n",
    "    def __init__(self, output_n: int):\n",
    "        self.output_n = output_n\n",
    "    \n",
    "    def sample_index(self, xyz):\n",
    "        \"\"\" Returns sample indices for pointcloud \"\"\"\n",
    "        N = xyz.shape[0]\n",
    "        centroids = np.zeros(self.output_n)\n",
    "        distance = np.ones(N) * 1e10\n",
    "        farthest = np.random.randint(0, N)\n",
    "        print(\"Sampling pointclouds ...\")\n",
    "        for i in tqdm(range(self.output_n)):\n",
    "            # Update the i-th farthest point\n",
    "            centroids[i] = farthest\n",
    "            # Take the xyz coordinate of the farthest point\n",
    "            centroid = xyz[farthest, :]\n",
    "            # Calculate the Euclidean distance from all points in the point set to this farthest point\n",
    "            dist = np.sum((xyz - centroid) ** 2, -1)\n",
    "            # Update distances to record the minimum distance of each point in the sample from all existing sample points\n",
    "            mask = dist < distance\n",
    "            distance[mask] = dist[mask]\n",
    "            # Find the farthest point from the updated distances matrix, and use it as the farthest point for the next iteration\n",
    "            farthest = np.argmax(distance, -1)\n",
    "        return centroids.astype(int)\n",
    "    \n",
    "    def __call__(self, pointcloud):\n",
    "        assert pointcloud.shape[1] == 4 # XYZI\n",
    "        xyz = pointcloud[:,:-1] # extract xyz\n",
    "        centroids = self.sample_index(xyz)\n",
    "        \n",
    "        sampled_pointcloud = pointcloud[centroids, :]\n",
    "        \n",
    "        return sampled_pointcloud"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc8ce8f1",
   "metadata": {},
   "source": [
    "### Lidar encoding (spherical projection + normal estimation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "96d5f294",
   "metadata": {},
   "outputs": [],
   "source": [
    "# velodyne config\n",
    "fov_up = 2 # deg\n",
    "fov_down = -24.8\n",
    "num_lasers = 64 # H\n",
    "img_length = 1800 # W"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e0777663",
   "metadata": {},
   "outputs": [],
   "source": [
    "class LidarEncoder():\n",
    "    def __init__(self, fov_up, fov_down, num_lasers: int, img_length: int):\n",
    "        self.num_lasers = num_lasers\n",
    "        self.img_length = img_length\n",
    "        \n",
    "        self.fov_up_rad = (fov_up / 180) * np.pi\n",
    "        self.fov_down_rad = (fov_down / 180) * np.pi\n",
    "        self.fov_rad = abs(self.fov_up_rad) + abs(self.fov_down_rad)\n",
    "    \n",
    "    def get_u_v(self, point):\n",
    "        assert point.shape[0] == 3 # XYZ\n",
    "\n",
    "        x, y, z = point\n",
    "        r = np.sqrt(x ** 2 + y ** 2 + z ** 2) # range\n",
    "        yaw = np.arctan2(y, x)\n",
    "        pitch = np.arcsin(z / r)\n",
    "\n",
    "        # Get projections in image coords and normalizing\n",
    "        v = 0.5 * (yaw / np.pi + 1.0)\n",
    "        u = 1.0 - (pitch + abs(self.fov_down_rad)) / self.fov_rad\n",
    "        # Scaling as per the lidar config given\n",
    "        v *= self.img_length;\n",
    "        u *= self.num_lasers;\n",
    "        # round and limit for use as index\n",
    "        v = np.floor(v);\n",
    "        v = min(self.img_length - 1, v);\n",
    "        v = max(0.0, v);\n",
    "        pixel_v = int(v); # col\n",
    "\n",
    "        u = np.floor(u);\n",
    "        u = min(num_lasers - 1, u);\n",
    "        u = max(0.0, u);\n",
    "        pixel_u = int(u); # row\n",
    "\n",
    "        return pixel_u, pixel_v, r\n",
    "    \n",
    "    def estimate_normals(self, pointcloud):\n",
    "        pointcloud_xyz = pointcloud[:,:-1]\n",
    "        pcd = o3d.geometry.PointCloud()\n",
    "        pcd.points = o3d.utility.Vector3dVector(pointcloud_xyz)\n",
    "        pcd.estimate_normals(search_param=o3d.geometry.KDTreeSearchParamKNN(knn=4)) # PCA w/ knn\n",
    "    \n",
    "        # o3d.visualization.draw_geometries([pcd], point_show_normal=True)\n",
    "        normals = np.asarray(pcd.normals)\n",
    "        return normals\n",
    "    \n",
    "    def __call__(self, pointcloud):\n",
    "        assert pointcloud.shape[1] == 4 # XYZI\n",
    "        N = pointcloud.shape[0]\n",
    "        projection = np.zeros((self.num_lasers, self.img_length, 8)) # feature_channels = 8\n",
    "        normals = self.estimate_normals(pointcloud) # estimate normals\n",
    "        \n",
    "        # create image projection # TODO: make this faster !!!\n",
    "        for i in range(N): \n",
    "            point = pointcloud[i,:-1] # grab XYZ\n",
    "            intensity = pointcloud[i,-1]\n",
    "            normal = normals[i, :] # xyz normals\n",
    "            pixel_u, pixel_v, r = self.get_u_v(point)\n",
    "            projection[pixel_u, pixel_v] = np.concatenate(([point[0], point[1], point[2], r, intensity], \n",
    "                                                            normal))\n",
    "    \n",
    "        # projection = projection[:, 4:1796, :] # crop resize to W=1792 as in paper\n",
    "        return projection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7bf54fe3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transforms --> [Sampler(), LidarEncoder(), ToTensor()]\n",
    "lidar_transform = transforms.Compose([LidarEncoder(fov_up, fov_down, num_lasers, img_length), \n",
    "                                      transforms.ToTensor()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "72f3bd6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# path to KITTI dataset \n",
    "scan_dir = '../../KITTI/data/dataset/sequences'\n",
    "pose_dir = '../../KITTI/data/dataset/poses'\n",
    "train_data = Kitti(scan_dir, pose_dir, transform=lidar_transform)\n",
    "val_data = Kitti(scan_dir, pose_dir, split='validate', transform=lidar_transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c276c022",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total frames: 1101, total poses: 1101 in train sequence 07\n",
      "Total frames: 271, total poses: 271 in validate sequence 04\n"
     ]
    }
   ],
   "source": [
    "print(train_data)\n",
    "print(val_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a40defa4",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataloader = DataLoader(dataset=train_data, batch_size=8, shuffle=True, drop_last=True)\n",
    "val_dataloader = DataLoader(dataset=val_data, batch_size=8, drop_last=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "id": "c205e3b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data.visualise(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "id": "d56a94f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "val_data.visualise(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f95c8aa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "curr_scan = train_data.load_velo(0)\n",
    "curr_img = train_data.projected[0]\n",
    "sampled_curr = PointCloudSampler(n_points)(curr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "142273c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "pcd = o3d.geometry.PointCloud()\n",
    "pcd.points = o3d.utility.Vector3dVector(sampled_curr[:,:-1])\n",
    "o3d.visualization.draw_geometries([pcd])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42830289",
   "metadata": {},
   "source": [
    "### Normal estimation (method from paper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f39c0bcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder = LidarEncoder(fov_up, fov_down, num_lasers, img_length)\n",
    "\n",
    "def weight(x1,x2):\n",
    "    _, _, r1 = encoder.get_u_v(x1)\n",
    "    _, _, r2 = encoder.get_u_v(x2)\n",
    "    range_diff = abs(r1 - r2)\n",
    "    return np.exp(-0.2*range_diff)\n",
    "\n",
    "def get_neighbors(img, p):\n",
    "    u,v, _ = encoder.get_u_v(p) # get pixel coords\n",
    "    u_end = img.shape[0]-1\n",
    "    v_end = img.shape[1]-1\n",
    "    # print(u,v)\n",
    "    \n",
    "    # get k=4 nearest-neighbors (up-down-left-right)\n",
    "    # TODO: change edge pixel cases to only 2-neighbors (better adjacent search overall)\n",
    "    if u == u_end or v == v_end:  \n",
    "        nn1 = img[max(u+1,0),v,:3]\n",
    "        nn2 = img[u,max(v-1,0),:3]\n",
    "        return np.array([nn1,nn2])\n",
    "    else:\n",
    "        nn1 = img[max(u-1,0),v,:3]\n",
    "        nn2 = img[u,max(v-1,0),:3]\n",
    "        nn3 = img[min(u+1,u_end),v,:3]\n",
    "        nn4 = img[u,min(v+1,v_end),:3]\n",
    "        return np.array([nn1, nn2, nn3, nn4])\n",
    "\n",
    "def get_normal(img, p):\n",
    "    neighbors = get_neighbors(img, p)\n",
    "    k = neighbors.shape[0] # k-neighbors\n",
    "    normal = 0\n",
    "    for i,j in itertools.combinations(range(k),2):\n",
    "        n_i = neighbors[i]\n",
    "        n_j = neighbors[j]\n",
    "        prod = np.cross(weight(n_i,p) * (n_i - p), \n",
    "            weight(n_j,p) * (n_j - p))\n",
    "        normal += prod\n",
    "\n",
    "    return (normal / np.linalg.norm(normal))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf5e2130",
   "metadata": {},
   "outputs": [],
   "source": [
    "N = curr_scan.shape[0]\n",
    "normals = np.zeros((N, 3))\n",
    "for i in tqdm(range(N)):\n",
    "    normal = get_normal(curr_img.permute(1,2,0).numpy(), curr_scan[i,:-1])\n",
    "    normals[i,:] = normal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "2bbae8b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "pcd = o3d.geometry.PointCloud()\n",
    "pcd.points = o3d.utility.Vector3dVector(curr_scan[:,:-1])\n",
    "pcd.normals = o3d.utility.Vector3dVector(normals)\n",
    "o3d.visualization.draw_geometries([pcd], point_show_normal=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "609127dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(122626, 3)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normals.shape # using method in paper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "49393410",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Open3D's method\n",
    "pcd = o3d.geometry.PointCloud()\n",
    "pcd.points = o3d.utility.Vector3dVector(curr_scan[:,:-1])\n",
    "pcd.estimate_normals(search_param=o3d.geometry.KDTreeSearchParamKNN(knn=4)) # PCA w/ knn\n",
    "o3d.visualization.draw_geometries([pcd], point_show_normal=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51c5edba",
   "metadata": {},
   "source": [
    "### Network model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9f536e9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "class FireConv(nn.Module):\n",
    "    \"\"\" FireConv layer\"\"\"\n",
    "    def __init__(self, inplanes: int, c1: int, c2: int, c3: int) -> None:\n",
    "        super(FireConv, self).__init__()\n",
    "        \n",
    "        self.relu = nn.ReLU(inplace=True)\n",
    "        self.squeeze = nn.Conv2d(inplanes, c1, kernel_size=1)\n",
    "        self.expand1x1 = nn.Conv2d(c1, c2, kernel_size=1)\n",
    "        self.expand3x3 = nn.Conv2d(c1,c3, kernel_size=3, padding=1)\n",
    "        \n",
    "    def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
    "        x = self.relu(self.squeeze(x))\n",
    "        return torch.cat([\n",
    "            self.relu(self.expand1x1(x)),\n",
    "            self.relu(self.expand3x3(x))], 1)\n",
    "        \n",
    "class SELayer(nn.Module):\n",
    "    \"\"\" Squeeze and Excitation layer from SEnet (channel attention) \"\"\"\n",
    "    def __init__(self, in_features: int, reduction=16) -> None:\n",
    "        super(SELayer, self).__init__()\n",
    "        self.avg_pool = nn.AdaptiveAvgPool2d(1) # 1x1 output size\n",
    "        self.fc = nn.Sequential(\n",
    "            nn.Linear(in_features, in_features // reduction, bias=False),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Linear(in_features // reduction, in_features, bias=False),\n",
    "            nn.Sigmoid())\n",
    "\n",
    "    def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
    "        b, c, _, _ = x.size()\n",
    "        y = self.avg_pool(x).view(b, c) # BxC\n",
    "        y = self.fc(y).view(b, c, 1, 1) # BxCx1x1\n",
    "        x_scaled = x * y.expand_as(x) # BxCxHxW\n",
    "        return x_scaled    \n",
    "\n",
    "    \n",
    "class ASPP(nn.Module):\n",
    "    def __init__(self, in_channels, atrous_rates, out_channels=128):\n",
    "        super(ASPP, self).__init__()\n",
    "        modules = []\n",
    "        modules.append(nn.Sequential(\n",
    "            nn.Conv2d(in_channels, out_channels, 1, bias=False), # conv1x1\n",
    "            nn.BatchNorm2d(out_channels),\n",
    "            nn.ReLU()))\n",
    "\n",
    "        rates = tuple(atrous_rates)\n",
    "        for rate in rates:\n",
    "            modules.append(ASPPConv(in_channels, out_channels, rate)) # conv w/ rate dilations\n",
    "\n",
    "        modules.append(ASPPPooling(in_channels, out_channels)) # global average pooling\n",
    "\n",
    "        self.convs = nn.ModuleList(modules)\n",
    "\n",
    "        self.project = nn.Sequential(\n",
    "            nn.Conv2d(5 * out_channels, out_channels, 1, bias=False), # conv1x1\n",
    "            nn.BatchNorm2d(out_channels),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(0.25))\n",
    "\n",
    "    def forward(self, x):\n",
    "        res = []\n",
    "        for conv in self.convs:\n",
    "            res.append(conv(x))\n",
    "        res = torch.cat(res, dim=1)\n",
    "        return self.project(res)\n",
    "\n",
    "    \n",
    "class ASPPConv(nn.Sequential):\n",
    "    def __init__(self, in_channels, out_channels, dilation):\n",
    "        modules = [\n",
    "            nn.Conv2d(in_channels, out_channels, 3, padding=dilation, dilation=dilation, bias=False),\n",
    "            nn.BatchNorm2d(out_channels),\n",
    "            nn.ReLU()\n",
    "        ]\n",
    "        super(ASPPConv, self).__init__(*modules)\n",
    "\n",
    "    \n",
    "class ASPPPooling(nn.Sequential):\n",
    "    def __init__(self, in_channels, out_channels):\n",
    "        super(ASPPPooling, self).__init__(\n",
    "            nn.AdaptiveAvgPool2d(1),\n",
    "            nn.Conv2d(in_channels, out_channels, 1, bias=False),\n",
    "            nn.BatchNorm2d(out_channels),\n",
    "            nn.ReLU())\n",
    "\n",
    "    def forward(self, x):\n",
    "        size = x.shape[-2:]\n",
    "        for mod in self:\n",
    "            x = mod(x)\n",
    "        return F.interpolate(x, size=size, mode='bilinear', align_corners=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "459ce1e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "class MaskEncoder(nn.Module):\n",
    "    \"\"\" mask prediction network encoder \"\"\"\n",
    "    def __init__(self, c: int) -> None:\n",
    "        super(MaskEncoder, self).__init__()\n",
    "\n",
    "        self.conv1 = nn.Conv2d(c, 64, kernel_size=3, stride=(1,2), padding=1)\n",
    "        self.pool1 = nn.MaxPool2d(kernel_size=3, stride=(1,2), padding=1)\n",
    "        self.fire1 = FireConv(64, 16, 64, 64)\n",
    "        self.fire2 = FireConv(128, 16, 64, 64)\n",
    "        self.se1 = SELayer(128, reduction=2)\n",
    "        \n",
    "        self.pool2 = nn.MaxPool2d(kernel_size=3, stride=(1,2), padding=1)\n",
    "        self.fire3 = FireConv(128, 32, 128, 128)\n",
    "        self.fire4 = FireConv(256, 32, 128, 128)\n",
    "        self.se2 = SELayer(256, reduction=2)\n",
    "        \n",
    "        self.pool3 = nn.MaxPool2d(kernel_size=3, stride=(1,2), padding=1)\n",
    "        self.fire5 = FireConv(256, 48, 192, 192)\n",
    "        self.fire6 = FireConv(384, 48, 192, 192)\n",
    "        self.fire7 = FireConv(384, 64, 256, 256)\n",
    "        self.fire8 = FireConv(512, 64, 256, 256)\n",
    "        self.se3 = SELayer(512, reduction=2)\n",
    "        \n",
    "        # Enlargement layer\n",
    "        self.aspp = ASPP(512, [6, 9, 12])\n",
    "    \n",
    "    def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
    "        x_c1 = F.relu(self.conv1(x), inplace=True)\n",
    "        x_p1 = self.pool1(x_c1)\n",
    "        x_f1 = self.fire1(x_p1)\n",
    "        x_f2 = self.fire2(x_f1)\n",
    "        x_se1 = self.se1(x_f2)\n",
    "        \n",
    "        x_p2 = self.pool2(x_se1)\n",
    "        x_f3 = self.fire3(x_p2)\n",
    "        x_f4 = self.fire4(x_f3)\n",
    "        x_se2 = self.se2(x_f4)\n",
    "        \n",
    "        x_p3 = self.pool3(x_se2)\n",
    "        x_f5 = self.fire5(x_p3)\n",
    "        x_f6 = self.fire6(x_f5)\n",
    "        x_f7 = self.fire7(x_f6)\n",
    "        x_f8 = self.fire8(x_f7)\n",
    "        x_se3 = self.se3(x_f8)\n",
    "        \n",
    "        x_el = self.aspp(x_se3)\n",
    "        return x_el"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "e2c3c5a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "class OdomRegNet(nn.Module):\n",
    "    \"\"\" Main odometry regression network - 2-stream net \"\"\"\n",
    "    def __init__(self, feature_channels=8):\n",
    "        super(OdomRegNet, self).__init__()\n",
    "\n",
    "        self.mask_encode = MaskEncoder(feature_channels) # [xyz range intensity normals]\n",
    "\n",
    "        self.fire_1 = FireConv(256, 64, 256, 256)\n",
    "        self.fire_2 = FireConv(512, 64, 256, 256)\n",
    "        self.se_1 = SELayer(512, reduction=2)\n",
    "        \n",
    "        self.pool_1 = nn.MaxPool2d(kernel_size=3, stride=(2,2), padding=1)\n",
    "        self.fire_3 = FireConv(512, 80, 384, 384)\n",
    "        self.fire_4 = FireConv(768, 80, 384, 384)\n",
    "        \n",
    "        self.pool_2 = nn.MaxPool2d(kernel_size=3, stride=(2,2), padding=1)\n",
    "        self.fc1 = nn.Linear(344064, 512)\n",
    "        self.dropout = nn.Dropout2d(p=0.5)\n",
    "        \n",
    "        self.fc2 = nn.Linear(512, 3)\n",
    "        self.fc3 = nn.Linear(512, 4) \n",
    "    \n",
    "    def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:\n",
    "        x_mask_out = torch.cat([self.mask_encode(x), self.mask_encode(y)], 1) # B, C',H, W\n",
    "        x_f1 = self.fire_1(x_mask_out) \n",
    "        x_f2 = self.fire_2(x_f1)\n",
    "        x_se = self.se_1(x_f2)\n",
    "        \n",
    "        x_p1 = self.pool_1(x_se)\n",
    "        x_f3 = self.fire_3(x_p1)\n",
    "        x_f4 = self.fire_4(x_f3)\n",
    "        \n",
    "        x_p2 = self.pool_2(x_f4)\n",
    "        x_p2 = x_p2.view(x_p2.size(0), -1) # flatten\n",
    "        x_fc1 = self.dropout(self.fc1(x_p2))\n",
    "        \n",
    "        x_out = self.fc2(x_fc1) # translation x\n",
    "        q_out = self.fc3(x_fc1) # rotation quarternion q\n",
    "        return x_out, q_out"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e685b68",
   "metadata": {},
   "source": [
    "### Loss function + eval metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "6b6ffb80",
   "metadata": {},
   "outputs": [],
   "source": [
    "class PoseLoss(nn.Module):\n",
    "    \"\"\" Geometric loss function from PoseNet paper \"\"\"\n",
    "    def __init__(self, sx, sq, eps=1e-6):\n",
    "        super(PoseLoss, self).__init__()\n",
    "        self.sx = nn.Parameter(torch.Tensor([sx]))\n",
    "        self.sq = nn.Parameter(torch.Tensor([sq]))\n",
    "        self.eps = eps # numerical stability during backprop\n",
    "        self.mse = nn.MSELoss() # TODO: try experimenting with L1-norm\n",
    "        \n",
    "    def forward(self, pred_x, pred_q, target_x, target_q):\n",
    "        pred_q = F.normalize(pred_q, p=2, dim=1)\n",
    "        loss_x = torch.sqrt(self.mse(target_x, pred_x) + self.eps) # L2-norm (RMSE)\n",
    "        loss_q = torch.sqrt(self.mse(target_q, pred_q) + self.eps)\n",
    "        \n",
    "        loss = torch.exp(-self.sx)*loss_x + self.sx \\\n",
    "               + torch.exp(-self.sq)*loss_q + self.sq\n",
    "        \n",
    "        return loss, loss_x, loss_q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "2d7bf326",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_x_q(pose: torch.Tensor):\n",
    "    \"\"\" Get x, q vectors from pose matrix \n",
    "    Args:\n",
    "        pose (Bx4x4 array): relative pose\n",
    "    Returns:\n",
    "        x (Bx3x1 array): translation \n",
    "        q (Bx4x1 array): quarternion\n",
    "    \"\"\"\n",
    "    x = pose[:, :-1, -1]\n",
    "    rot = pose[:, :-1, :-1] \n",
    "    r = R.from_matrix(rot.detach().numpy())\n",
    "    q = torch.from_numpy(r.as_quat())\n",
    "    \n",
    "    return x.float(), q.float()\n",
    "\n",
    "def get_pose(x, q):\n",
    "    \"\"\" Get 4x4 pose from x and q numpy vectors\n",
    "    Args:\n",
    "        x (3x1 array): translation \n",
    "        q (4x1 array): quarternion\n",
    "    Returns:\n",
    "        pose (4x4 array): transformation pose\n",
    "    \"\"\"\n",
    "    pose = np.identity(4)\n",
    "    r = R.from_quat(q)\n",
    "    rot = r.as_matrix()\n",
    "    pose[:-1, :-1] = rot\n",
    "    pose[:-1, -1] = x\n",
    "    \n",
    "    return pose\n",
    "\n",
    "def rotation_error(pose_error):\n",
    "    \"\"\" Compute rotation error\n",
    "    Args:\n",
    "        pose_error (4x4 array): relative pose error\n",
    "    Returns:\n",
    "        rot_error (float): rotation error\n",
    "    \"\"\"\n",
    "    a = pose_error[0, 0]\n",
    "    b = pose_error[1, 1]\n",
    "    c = pose_error[2, 2]\n",
    "    d = 0.5*(a+b+c-1.0)\n",
    "    rot_error = np.arccos(max(min(d, 1.0), -1.0))\n",
    "    return rot_error\n",
    "\n",
    "def translation_error(pose_error):\n",
    "    \"\"\" Compute translation error\n",
    "    Args:\n",
    "        pose_error (4x4 array): relative pose error\n",
    "    Returns:\n",
    "        trans_error (float): translation error\n",
    "    \"\"\"\n",
    "    dx = pose_error[0, 3]\n",
    "    dy = pose_error[1, 3]\n",
    "    dz = pose_error[2, 3]\n",
    "    trans_error = np.sqrt(dx**2+dy**2+dz**2)\n",
    "    return trans_error"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30af3e90",
   "metadata": {},
   "source": [
    "### Training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ad7405ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cpu\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "torch.set_num_threads(1)\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "15bb5f28",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = OdomRegNet().to(device)\n",
    "criterion = PoseLoss(sx=0.0, sq=-2.5).to(device)\n",
    "optimizer = torch.optim.Adam([{'params': model.parameters()},\n",
    "                              {'params': [criterion.sx, criterion.sq]}],\n",
    "                             lr=0.001)\n",
    "lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1) # TODO: maybe try ReduceOnPlateau()?\n",
    "writer = SummaryWriter(log_dir='summary_LO_net')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "5f96ffd7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total model parameters: 180576935\n"
     ]
    }
   ],
   "source": [
    "print(\"Total model parameters: {}\".format(sum([np.prod(p.shape) for p in model.parameters()])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "2c42e28f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(model, train_dataloader, val_dataloader=None, epochs=30, checkpoint_dir=None):\n",
    "    # Pre-define variables to load/save best model\n",
    "    best_train_loss = 10000\n",
    "    if checkpoint_dir and os.path.isfile(checkpoint_dir):\n",
    "        print(\"=> Loading checkpoint from '{}'\".format(checkpoint_dir))\n",
    "        \n",
    "        checkpoint = torch.load(checkpoint_dir, map_location=device)\n",
    "        start_epoch = checkpoint['epoch']\n",
    "        best_val_loss = checkpoint['best_val_loss']\n",
    "        model.load_state_dict(checkpoint['state_dict'])\n",
    "        optimizer.load_state_dict(checkpoint['optimizer'])\n",
    "        lr_scheduler.load_state_dict(checkpoint['lr'])\n",
    "        \n",
    "        print(\"=> Loaded checkpoint. Resuming from epoch {}\".format(start_epoch+1))\n",
    "        print(\"=> Best val loss = {:.6f}\".format(best_val_loss))\n",
    "    \n",
    "    else:\n",
    "        print(\"=> No checkpoint found.\".format(checkpoint_dir))\n",
    "        \n",
    "        start_epoch = 0\n",
    "        best_val_loss = 10000\n",
    "        \n",
    "    print('#' * 40)\n",
    "    print(\"Starting training ... \")\n",
    "    print('#' * 40)\n",
    "    \n",
    "    for epoch in range(start_epoch, epochs):\n",
    "        model.train()\n",
    "        start_time = time.time() # epoch start time\n",
    "        error_train = []\n",
    "        error_val = []\n",
    "        \n",
    "        for i, inputs in enumerate(train_dataloader, start=1):\n",
    "            step_val = epoch * len(val_dataloader) + i\n",
    "            print('[Epoch: %d / %d, Batch: %4d / %4d]' %(epoch + 1, epochs, i, len(train_dataloader)))\n",
    "            curr_img, prev_img, pose_gt = inputs\n",
    "            curr_img, prev_img, pose_gt = \\\n",
    "                        curr_img.to(device).float(), prev_img.to(device).float(), pose_gt.to(device).float()\n",
    "            \n",
    "            x_out, q_out = model(curr_img, prev_img) # predicted pose\n",
    "            x_true, q_true = get_x_q(pose_gt) # true pose\n",
    "            \n",
    "            loss, loss_x, loss_q = criterion(x_out, q_out, x_true, q_true)\n",
    "            print('sx = {:.6f}, sq = {:.6f}'.format(criterion.sx.item(), criterion.sq.item()))\n",
    "            \n",
    "            optimizer.zero_grad()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            \n",
    "            error_train.append(loss.item())\n",
    "            print('Train loss: total loss {:.6f} / x-loss {:.6f} / q-loss {:.6f}'.format(loss.item(), loss_x.item(), loss_q.item()))\n",
    "            print()\n",
    "            \n",
    "            writer.add_scalars(\"train_loss\", {'total':loss.item(),\n",
    "                                              'x':loss_x.item(),\n",
    "                                              'q': loss_q.item()}, step_val)\n",
    "            writer.add_scalars(\"optim\", {'sx': criterion.sx.item(),\n",
    "                                         'sq': criterion.sq.item()}, step_val)\n",
    "            \n",
    "            if i == 30: # train on only 30 batches\n",
    "                break\n",
    "\n",
    "        lr_scheduler.step()\n",
    "        print(\"LR: {}\".format(lr_scheduler.get_last_lr()[0]))\n",
    "        writer.add_scalar(\"LR\", lr_scheduler.get_last_lr()[0], epoch)\n",
    "        \n",
    "        if val_dataloader:\n",
    "            print('#' * 40)\n",
    "            print(\"Validating ... \")\n",
    "            print('#' * 40)\n",
    "            model.eval()\n",
    "            \n",
    "            with torch.no_grad():\n",
    "                for i, inputs in enumerate(val_dataloader, start=1):\n",
    "                    step_val = epoch * len(val_dataloader) + i\n",
    "                    print('[Epoch: %d / %d, Batch: %4d / %4d]' %(epoch + 1, epochs, i, len(val_dataloader)))\n",
    "                    curr_img, prev_img, pose_gt = inputs\n",
    "                    curr_img, prev_img, pose_gt = \\\n",
    "                            curr_img.to(device).float(), prev_img.to(device).float(), pose_gt.to(device).float()\n",
    "                    \n",
    "                    x_out, q_out = model(curr_img, prev_img)\n",
    "                    x_true, q_true = get_x_q(pose_gt)\n",
    "                    \n",
    "                    loss, loss_x, loss_q = criterion(x_out, q_out, x_true, q_true)\n",
    "                    \n",
    "                    error_val.append(loss.item())\n",
    "                    print('Val loss: total loss {:.6f} / x-loss {:.6f} / q-loss {:.6f}'.format(loss.item(), loss_x.item(), loss_q.item()))\n",
    "                    print()\n",
    "                    \n",
    "                    writer.add_scalars(\"val_loss\", {'total':loss.item(),\n",
    "                                              'x':loss_x.item(),\n",
    "                                              'q': loss_q.item()}, step_val)\n",
    "                    \n",
    "        error_train_loss = np.mean(error_train) # avg. of accumulated losses per epoch\n",
    "        error_val_loss = np.mean(error_val)\n",
    "        \n",
    "        print('[Epoch: {} ==> Train error {:.6f} / Validation error {:.6f}]'.format(epoch + 1, \n",
    "                                                                                    error_train_loss, error_val_loss))\n",
    "        \n",
    "        writer.add_scalars('loss/trainval', {'train':error_train_loss,\n",
    "                                                     'val':error_val_loss}, epoch)\n",
    "        \n",
    "        if error_train_loss < best_train_loss:\n",
    "            best_train_loss = error_train_loss\n",
    "        if error_val_loss < best_val_loss:\n",
    "            best_val_loss = error_val_loss\n",
    "            print()\n",
    "            print(\"Saving model to new checkpoint ...\")\n",
    "            print('=' * 40)\n",
    "            print()\n",
    "            state = {\n",
    "                'epoch': epoch + 1,\n",
    "                'state_dict': model.state_dict(),\n",
    "                'best_val_loss': best_val_loss,\n",
    "                'optimizer': optimizer.state_dict(),\n",
    "                'lr': lr_scheduler.state_dict()\n",
    "            }\n",
    "            torch.save(state, \"best_epoch_model.pth\")\n",
    "            \n",
    "        time_elapsed = time.time() - start_time\n",
    "        print('Epoch {} completed in {:.0f}m {:.0f}s'.format(epoch + 1, time_elapsed // 60, time_elapsed % 60))\n",
    "        print('+' * 40)\n",
    "        print()\n",
    "        \n",
    "    print('#' * 40)\n",
    "    print('#' * 40)\n",
    "    print(\"Training completed :)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca538660",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "model_dir = 'best_epoch_model.pth'\n",
    "train(model, train_dataloader, val_dataloader, checkpoint_dir=model_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "f38d8c92",
   "metadata": {},
   "outputs": [],
   "source": [
    "# x = torch.rand((1, 8, 64, 1792)).to(device)\n",
    "# y = torch.rand((1, 8, 64, 1792)).to(device)\n",
    "# writer.add_graph(model, (x,y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "4a9df3dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext tensorboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "991f835f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "      <iframe id=\"tensorboard-frame-31e17a72355d6beb\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
       "      </iframe>\n",
       "      <script>\n",
       "        (function() {\n",
       "          const frame = document.getElementById(\"tensorboard-frame-31e17a72355d6beb\");\n",
       "          const url = new URL(\"/\", window.location);\n",
       "          const port = 6006;\n",
       "          if (port) {\n",
       "            url.port = port;\n",
       "          }\n",
       "          frame.src = url;\n",
       "        })();\n",
       "      </script>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%tensorboard --logdir 'summary_LO_net'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abd22ef3",
   "metadata": {},
   "source": [
    "### Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "id": "76e8780d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def test(model, dataloader, test_model_path: str):\n",
    "    model.eval()\n",
    "    if os.path.isfile(test_model_path):\n",
    "        print('=> Loading pretrained model at {}'.format(test_model_path))\n",
    "        test_checkpoint = torch.load(test_model_path, map_location=device)\n",
    "        model.load_state_dict(test_checkpoint['state_dict'])\n",
    "        print(\"=> Loaded model\")\n",
    "        print(\"=\" * 40)\n",
    "    else:\n",
    "        print(\"=> No model found at {}\".format(test_model_path))\n",
    "        return \n",
    "    \n",
    "    estim_poses = []\n",
    "    gt_poses = []\n",
    "    trans_errors = []\n",
    "    rot_errors = []\n",
    "    \n",
    "    for i, inputs in enumerate(dataloader, 1):\n",
    "        print('[Batch: %4d / %4d]' %(i, len(dataloader)))\n",
    "        curr_img, prev_img, pose_gt = inputs\n",
    "        curr_img, prev_img, pose_gt = \\\n",
    "                curr_img.to(device).float(), prev_img.to(device).float(), pose_gt.to(device).float()\n",
    "\n",
    "        x_out, q_out = model(curr_img, prev_img)\n",
    "        x_out = x_out.squeeze(0).detach().numpy()\n",
    "        q_out = F.normalize(q_out, p=2, dim=1).squeeze(0).detach().numpy()\n",
    "        pose_out = get_pose(x_out, q_out)\n",
    "        \n",
    "        x_true, q_true = get_x_q(pose_gt)\n",
    "        x_true = x_true.squeeze(0).numpy()\n",
    "        q_true = q_true.squeeze(0).numpy()\n",
    "        pose_true = pose_gt.squeeze(0).detach().numpy()\n",
    "        \n",
    "        print('x out: {}, q out: {}'.format(x_out, q_out))\n",
    "        print('x true: {}, q true: {}'.format(x_true, q_true))\n",
    "        \n",
    "        pose_error = np.linalg.inv(pose_true) @ pose_out # relative pose error\n",
    "        trans_error = translation_error(pose_error) \n",
    "        rot_error = rotation_error(pose_error) \n",
    "        \n",
    "        estim_poses.append(pose_out)\n",
    "        gt_poses.append(pose_true)\n",
    "        \n",
    "        trans_errors.append(trans_error)\n",
    "        rot_errors.append(rot_error)\n",
    "        \n",
    "        print(\"Relative Pose Errors: translation: {:.6f} / rotation {:.6f}\".format(trans_error, rot_error))\n",
    "        print()\n",
    "    \n",
    "    print()\n",
    "    print('+' * 40)\n",
    "    print(\"Testing complete !!\")\n",
    "    print(\"Overall RMSE pose errors: translation {:.6f} / rotation {:.6f}\".format(np.sqrt(np.mean(np.asarray(trans_errors) ** 2)),\n",
    "                                                                                  np.sqrt(np.mean(np.asarray(rot_errors) ** 2))))\n",
    "    print('+' * 40)\n",
    "        \n",
    "    return estim_poses, gt_poses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "354dd082",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total frames: 1101, total poses: 1101 in test sequence 06\n"
     ]
    }
   ],
   "source": [
    "test_data = Kitti(scan_dir, pose_dir, split='test', test_sequence='06'\n",
    "                  , transform=lidar_transform)\n",
    "test_dataloader = DataLoader(dataset=test_data, batch_size=1)\n",
    "print(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e62df72",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "result_06, gt_06 = test(model, test_dataloader, test_model_path='best_epoch_model.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "id": "622cd7e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save pose array as txt\n",
    "f_result = 'result_06.npy'\n",
    "np.save(f_result, result_06)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "id": "b9f66073",
   "metadata": {},
   "outputs": [],
   "source": [
    "rel = np.load('result_06.npy')\n",
    "rel_gt = gt_06"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "id": "4af7a5b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reconstruct global poses from odometry\n",
    "assert len(rel) == len(rel_gt)\n",
    "pred = np.zeros((len(rel), 4, 4))\n",
    "gt = np.zeros((len(rel), 4, 4))\n",
    "for i in range(len(rel)):\n",
    "    if i == 0:\n",
    "        pred[i] = pred_prev = np.identity(4)\n",
    "        gt[i] = gt_prev = rel_gt[i]\n",
    "        continue\n",
    "        \n",
    "    pred[i] = pred_prev @ rel[i]\n",
    "    gt[i] = gt_prev @ rel_gt[i]\n",
    "    pred_prev = pred[i]\n",
    "    gt_prev = gt[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "id": "ec8615d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "id": "a55e8ab3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fcc6dbac390>"
      ]
     },
     "execution_count": 447,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,6))\n",
    "ax = plt.axes(projection='3d')\n",
    "# fig, ax = plt.subplots(figsize=(12,6))\n",
    "ax.plot(gt[:, :, 3][:, 0], gt[:, :, 3][:, 1], gt[:, :, 3][:, 2], label='gt')\n",
    "ax.plot(pred[:,:, 3][:, 0], pred[:, :, 3][:, 1], pred[:, :, 3][:, 2], label='pred')\n",
    "ax.title.set_text(\"Sequence 06\")\n",
    "\n",
    "ax.set_xlabel('x (m)')\n",
    "ax.set_ylabel('y (m)')\n",
    "ax.set_zlabel('z (m)')\n",
    "ax.legend()\n",
    "\n",
    "# ax.set_xlim([-100,100])\n",
    "# ax.view_init(elev=-80, azim=270)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "id": "47220b70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-100.0, 100.0)"
      ]
     },
     "execution_count": 454,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fig = plt.figure(figsize=(12,6))\n",
    "# ax = plt.axes(projection='3d')\n",
    "fig, ax = plt.subplots(figsize=(12,6))\n",
    "ax.plot(gt[:, :, 3][:, 0], gt[:, :, 3][:, 2], label='gt')\n",
    "ax.plot(pred[:,:, 3][:, 0], pred[:, :, 3][:, 2], label='pred')\n",
    "ax.title.set_text(\"Sequence 06\")\n",
    "\n",
    "ax.set_xlabel('x (m)')\n",
    "ax.set_ylabel('z (m)')\n",
    "# ax.set_zlabel('z (m)')\n",
    "ax.legend()\n",
    "ax.set_xlim([-100,100])\n",
    "# ax.view_init(elev=-80, azim=270)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f6f3e0d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8e91b2d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4782a682",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e04ddd5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74fd8362",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37a56ece",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4e5e281",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19834439",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
